Understanding Dynamics of Truck Co-Driving Networks
conference paper
The goal of this paper is to learn the dynamics of truck co-driving behaviour. Understanding this behaviour is important because co-driving has a potential positive impact on the environment. In the so-called co-driving network, trucks are nodes while links indicate that two trucks frequently drive together. To understand the network’s dynamics, we use a link prediction approach employing a machine learning classifier. The features of the classifier can be categorized into spatio-temporal features, neighbourhood features, path features, and node features. The very different types of features allow us to understand the social processes underlying the co-driving behaviour. Our work is based on a spatio-temporal data not studied before. Data is collected from 18 million truck movements in the Netherlands. We find that co-driving behaviour is best described by using neighbourhood features, and to lesser extent by path and spatio-temporal features. Node features are deemed unimportant. Findings suggest that the dynamics of a truck co-driving network has clear social network effects. © 2020, Springer Nature Switzerland AG.
TNO Identifier
878348
ISSN
1860949X
ISBN
9783030366827
Publisher
Springer
Source title
Studies in Computational Intelligence, 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019, 10 December 2019 through 12 December 2019
Pages
140-151
Files
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